Random data is well known for being used in, for example, simulations or probability calculations. Since generating truly random data is difficult, pseudo-random numbers computed or generated by computers are commonly-used as the random data. However, using pseudo-random numbers often causes an increase in the amount of computation and a reduction in accuracy in the computation results, because the computation results may converge on unrealistic local solutions.
In view of the above, various hardware and software solutions have been adopted for accurately generating random data. For example, achieving TFlops computation capabilities and speeding up inter-PU (Processing Unit) communications have been adopted as hardware solutions, and modified algorithms for the modified Newton method have been adopted as software solutions. Even thermal phenomena in semiconductor devices have been used for generating random data.
These approaches, however, do not suffice from the perspectives of convenience and cost, and there is a demand for new approaches. Whatever the benefits of previous random data generating techniques they do not share the advantages of the following techniques and tools.